Course overview
Practical model development, datasets, training and evaluation.
Course code: AI-201 Proposed duration: 12 weeks Level: Intermediate
Prerequisites: AI-101 or equivalent Python experience
Status: Curriculum in development; not open for enrollment.
Learning outcomes
- Explain and apply the core concepts covered in this subject
- Complete guided practical exercises in an appropriate test environment
- Document technical methods, findings and limitations
- Demonstrate understanding through a final project
Proposed weekly syllabus
- Week 1: Python review
- Week 2: Data collection
- Week 3: Data cleaning
- Week 4: Exploratory analysis
- Week 5: Regression
- Week 6: Classification
- Week 7: Training workflows
- Week 8: Model evaluation
- Week 9: Overfitting and validation
- Week 10: Model deployment
- Week 11: Responsible ML
- Week 12: Final machine-learning project
Proposed assessments
- Weekly practice exercises and knowledge checks
- Practical lab documentation
- Final project with an assessment rubric
- Review of technical accuracy, safety and clarity
Final project
Train, evaluate and document a machine-learning model.
Course delivery and availability
This is a proposed curriculum outline, not a currently offered credential-bearing course. Tuition, instructor assignments, timetables, enrollment and formal assessment arrangements have not been established.